This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water reactor (PWR). A full lower-plenum and core-inlet domain was constructed using publicly available geometry and operating conditions, enabling transient simulations with pump-induced swirl boundary conditions. The results show that cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region, while axial resistance and mixing progressively homogenize the flow at higher elevations. These physics-informed datasets were subsequently used to evaluate machine learning (ML) applications for partial field reconstruction and short-term autoregressive prediction. A 3D convolutional-based inpainting model successfully recon-structed missing assembly-level mass flow rates from partial observations, with errors concentrated in the highly turbulent base (bottom) layer and diminishing significantly in upper layers. Comparative analysis across multiple ML models demon-strates that spatially aware architectures, particularly ConvLSTM, significantly outperform sequence-based (LSTM) and operator-learning (DeepONet) approaches by effectively capturing coupled spatio-temporal dynamics. The study also high-lights key challenges, including the sensitivity of inlet flow predictions to turbulence and mesh resolution, as well as the absence of full-scale experimental validation data. Despite these limitations, the results remain consistent with expected physical behavior. Overall, this work establishes high-fidelity CFD as a critical foundation for developing data-driven surrogates, sparse sensing strategies, and future multiphysics coupling frameworks.

In tightly coupled multiphysics simulations, the conventional Monte Carlo assumption of piecewise-constant material properties is no longer valid, as temperature and density fields vary continuously in space. Current approaches to this problem each impose limitations on accuracy, generality, or computational efficiency. This work introduces the Lagrange Error-Adaptive Collision Sampling (LEACS) method, which addresses spatially varying material properties during Monte Carlo neutron transport by dynamically evaluating the coupled finite element solution along the particle’s flight path. Rather than converting the multiphysics solution into a lower-order representation, LEACS accesses native element shape functions directly, with sample positions chosen analytically via the Lagrange error bound to optimally minimize error in the reconstructed collision probability distribution. LEACS is implemented in OpenMC and demonstrated on pin cell and assembly test cases and coupled with MOOSE thermal solutions. Analytical error analysis confirms that Lagrange-adaptive spacing consistently outperforms equidistant spacing, with the greatest gains observed for long axial flight paths. Spatial tally results suggest that as few as three sample points per flight segment are sufficient to capture the radial and axial impact of temperature gradients on the (n,γ) reaction rate distribution. Computational cost analysis reveals slowdown factors vary depending on the model geometry, with finite element point location and inverse mapping as the dominant cost components and primary targets for future optimization.

This paper provides an initial description of the Method of Simultaneous Solutions, a Monte Carlo approach that simultaneously solves multiple Boltzmann-transport-like phenomena. Here, it is used to simultaneously solve the neutron transport and heat conduction equations. Analytically-derived weighting factors are tracked through a neutron transport-governed random walk to tally statistical estimators that can be used to calculate the temperature distribution. In this initial presentation, the method is readily applicable to neutron-heat multiphysics problems where the heat source and neutron source are identically distributed spatially. The primary theoretical benefit of MOSS lies in the reduction of computational cost that occurs from the removal of a dedicated routine to solve the heat conduction equation. Practically, branching processes required to capture the disparate boundary conditions associated with these separate physical phenomena can lead to large computational times dedicated to a single physics. In addition, this correlated sampling-based method can suffer from infinite variance associated with statistical estimators if the stochastic processes being tracked are too different. The final drawback demonstrated in this paper is that the approximation of heat conduction as a Boltzmann transport-governed process leads to errors in calculated temperatures. The paper explores these drawbacks on two demonstration problems, a problem consisting of slab geometry and a problem consisting of a hexagonal pin cell.

This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water reactor (PWR). A full lower-plenum and core-inlet domain was constructed using publicly available geometry and operating conditions, enabling transient simulations with pump-induced swirl boundary conditions. The results show that pump-induced cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region, while axial resistance and mixing progressively homogenize the flow at higher elevations.These physics-informed datasets were subsequently used to evaluate machine learning (ML) applications for partial field reconstruction and short-term autoregressive prediction. A 3D convolution-based inpainting model successfully reconstructed missing assembly-level mass flow rates from partial observations, with errors concentrated in the highly turbulent base (bottom) layer and diminishing significantly in upper layers. Comparative analysis across multiple ML models demonstrates that spatially aware architectures, particularly ConvLSTM, significantly outperform sequence-based (LSTM) and operator-learning (DeepONet) approaches by effectively capturing coupled spatio-temporal dynamics. The study also highlights key challenges, including the sensitivity of inlet flow predictions to turbulence and mesh resolution, as well as the absence of full-scale experimental validation data. Despite these limitations, the results remain consistent with expected physical behavior. Overall, this work establishes high-fidelity CFD as a critical foundation for developing data-driven surrogates, sparse sensing strategies, and future multiphysics coupling frameworks, while outlining directions for scalable, interpretable, and multi-fidelity learning approaches in nuclear reactor modeling.

Sensitivity analysis capabilities have yet to find extensive use in fusion reactor design applications where they can help understand the impact of nuclear data uncertainties on the tritium breeding ratio (TBR), shutdown dose rates, and nuclear heating. Significant uncertainty exists in nuclear data for fusion applications, and the goal of this work is to explore whether adjoint- and perturbation theory-based eigenvalue and generalized response sensitivity methods recently developed within the OpenMC Monte Carlo code can be extended to fixed-source Monte Carlo radiation transport simulations.This paper presents a derivation for the extended sensitivity analysis method. The adjoint-based sensitivity coefficients are compared with Monte Carlo SERPENT sensitivity coefficients for a TBR calculation in a simplified ARC-class tokamak. Further verification with the Monte Carlo MCSEN and deterministic ASUSD sensitivity coefficients for the tritium production rate in the Frascati neutron generator helium-cooled pebble bed test blanket module mock-up experiment was performed. The OpenMC sensitivity coefficients were found to agree with the other code systems. The use of the sensitivity coefficients with nuclear data covariance within the sandwich rule for cross-section uncertainty propagation also showed good agreement with the reference total Monte Carlo nuclear data uncertainty.

The phonon sampling method (PSM) enables accurate and efficient simulation of thermal neutron scattering from solids; it obviates a substantial degree of discretization, enabling continuous representations of scattering law behavior across a variety of conditions. Despite these strengths, the PSM relies on the phonon expansion, which only applies to solid materials. To remove this limitation, we demonstrate an efficient sampling algorithm for the nonsymmetric translational ${\mathcal S}(\alpha, \beta)$S(alpha,beta) distribution of Egelstaff and Schofield, and show how this can be used to extend the PSM for the simulation of thermal neutron scattering in materials exhibiting mixed translational and vibrational behavior, such as water or molten salts. We demonstrate the correctness of the method with example results for scattering from room temperature water at a few incident neutron energies.

The Random Ray Method (TRRM) neutron transport solver is a robust variation of the Method of Characteristics (MOC) for which ray sampling is uniform in space and angle, as opposed to the typical fixed quadrature cyclical ray tracking. However, there are several classes of problems for which the implicitly assumed uniform sampling distribution of TRRM may not be optimal. This work introduces the Ray Adaptive Stochastic Transport (RASTr) method, which computes statistical weights for an arbitrary spatially and angularly biased ray sampling distribution to provide variance reduction for problems where non-uniform sampling is desirable. The RASTr algorithm is implemented and tested on 1D and 2D test cases with demonstrated improvement in statistical relative uncertainty.

A static alpha eigenvalue capability was implemented in a modified version of the open-source Monte Carlo radiation transport code OpenMC and validated against Rossi-alpha measurements from 21 delayed-critical benchmark experiments and 33 subcritical configurations spanning fast, intermediate, and thermal systems with U-233, HEU, IEU, LEU, and plutonium fuels. The effective delayed neutron fraction was calculated using the k-prompt method, and the prompt neutron lifetime was calculated using the iterated fission probability method, both evaluated within the standard k-eigenvalue power iteration. The delayed-critical alpha eigenvalue was calculated from these quantities using the point kinetics equation alpha_dc = -beta_eff / ell_p. Agreement was generally within 10

Long-lived fission products represent a major challenge in nuclear waste management due to persistent radiotoxicity over very long timescales. This study focuses on six of these fission products: Se-79, Zr-93, Tc-99, Sn-126, I-127, Cs-135. This study investigates the feasibility of spallation-driven transmutation, in which a high energy proton beam strikes a heavy spallation target to generate neutrons that induce transmutation in the fission products surrounding the target. Lead and depleted uranium are identified as the principal spallation target candidates, reflecting contrasting trade offs in neutron yield, secondary reactions, and heat generation. Simulations assess nuclide specific behavior under reactor scale inventories and practical geometric constraints. Results demonstrate that technetium, iodine, and selenium are strong candidates for transmutation using this pathway, while tin shows partial resistance but benefits from thermal flux. By contrast, zirconium is inefficient to transmute, and cesium suffers from low net reduction due to competition with lighter isotopes. Cost effectiveness is highly isotope-dependent: technetium is most favorable, whereas cesium and zirconium remain expensive. These findings highlight the advantages and limitations of spallation driven systems and motivate strategies with optimized target-blanket designs.

We first review the requirements of Monte Carlo neutron transport codes to accurately model the phenomena associated with the unresolved resonance regime and present the first rigorous justification for the modeling assumption employed by probability tables for unresolved resonances. We present a new method named AURA (analytic unresolved resonance algorithm) for modeling unresolved resonance cross-sections with the normal inverse Gaussian distribution. The new method accurately models temperature dependence in the unresolved resonance region (URR), requires fewer data than previous methods, and outperforms the conventional URR treatment on GPUs.

OpenMC can be used to computationally model depletion and produce estimates of decay heat. As an input to depletion simulations, OpenMC requires a depletion chain that details nuclide transmutation pathways. The simplified CASL depletion chain was designed to track relatively few nuclides while still accurately modeling the effective neutron multiplication factor and nuclide number densities. However, the CASL chain dramatically underestimates decay heat due to the many nuclides it does not contain. In this work, we modify the CASL depletion chain to improve its accuracy while maintaining its computational efficiency. We demonstrate the effectiveness of adding pseudo-nuclides to the CASL chain, with each pseudo-nuclide capturing the behavior of a large group of nuclides. We further introduce "delay nuclides," which dramatically improve the accuracy of decay heat estimates.

Multiphysics analysis has become a common technique for nuclear reactor design validation, with neutronic-thermal analysis being the typical choice for understanding reactor dynamics. The concept of adding mechanical simulation such as thermal expansion to this coupling is still relatively new, however, and presents many computational challenges. While large reactors see relatively little neutronic impact from thermal expansion and may not warrant the challenge of undertaking this level of coupling, recent studies of microreactor geometries show that smaller reactors see larger impacts from thermal expansion. This work performs coupled neutronic-thermal-mechanical simulation of the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) using OpenMC and Multiphysics Object-Oriented Simulation Environment in order to analyze the neutronic and thermal impact of including thermal expansion at steady state. The results show that while thermal expansion has a significant effect on global neutronic tallies, it has relatively minor impact on spatial heating rates or temperatures in the system. This remains true even when simulating a multiple heat pipe failure scenario to introduce thermal asymmetry.

Zirconium hydride is a solid moderator material that has a high (albeit variable) hydrogen content. Proper modeling of the thermal neutron population in zirconium hydride requires that the thermal neutron scattering law be used, which incorporates the material structure into the cross section. In the incoherent approximation, this scattering law maybe calculated using a material's phonon distribution and other material parameters. Historically, the partial phonon distributions for zirconium hydride (ZrHx) have often been assumed to be independent of hydrogen content, crystalline phase, and temperature. The present work aims to relax this approximation by generating phonon distributions using molecular dynamics while varying (1) the hydrogen content, (2) crystalline phase, and (3) material temperature. Through this work, hydrogen content and crystalline phase have shown to have mild impacts on the vibrational structure, while temperature appears to hold strong influence over the vibrational properties of the materials considered. Temperature-dependent H(ZrHx) phonon distributions for delta-ZrH1.67, epsilon-ZrH1.82, and epsilon-ZrH2 were tested on models of a TRIGA reactor and a SNAP reactor, showing how material-specific vibrational models can noticeably influence k(eff), flux distributions, and reactivity coefficients.

This paper presents an efficient Monte Carlo mode for simulating subcritical systems with external sources. While solving these systems as a fixed source is possible, the length of the histories grows significantly as the system nears criticality, making run time significant. Instead, a hybrid method is proposed that leverages the traditional eigensolver while including elements of the external source. The method builds on prior work,but proposes an approach that maintains the size of the source bank and also provides a natural way of scaling tallies with the true multiplication factor. The method is demonstrated on a subcritical sphere with varying point source position and energy spectrum, as well as an approach to criticality problem. The results demonstrate good agreement with the fixed-source mode, with much improved particle tracking rates for near-critical problems.

The purpose of this paper is to introduce a new approach to compute nuclear data uncertainties called Embedded Monte Carlo (EMC) and compare it to the well-established Total Monte Carlo (TMC) method. While the TMC methodology involves generating numerous random nuclear data library samples and conducting separate Monte Carlo simulations for each, this approach calculates nuclear data uncertainties by subtracting statistical uncertainties from the total uncertainties of each simulation. The EMC method addresses the challenge of statistical uncertainty where each batch represents a new random sample, thereby embedding the propagation of uncertainties within a single calculation and reducing computational costs. This technique also enables the calculation of nuclear data uncertainties by leveraging a combination of history and batch statistics in eigenvalue calculations. This paper demonstrates the potential of the EMC method using OpenMC with an analysis performed on two different benchmarks by propagating the uncertainty on three input parameters: the average neutron multiplicity νν ν ν $ \overline{\nu} $ , the prompt neutron fission spectrum (PFNS) χ and the 239Pu density.

Zirconium hydride is a widely used moderator in compact reactor designs due to its high thermal limits and high hydrogen density, both of which being desirable feature. However, a notable characteristic of zirconium hydride is the substantial mobility of hydrogen within the metal lattice, especially at high temperatures and under large thermal gradients. Variations in hydrogen distribution can significantly affect neutron moderation and, consequently, the reactor’s power profile. This study employs a coupled OpenMC-MOOSE simulation framework to model this complex feedback between hydrogen transport, heat transfer, and neutron behavior. A hypothetical epithermal reactor configuration is analyzed, where zirconium hydride serves as a monolithic moderator with embedded fuel pins and heat pipes. The simulation results illustrate the redistribution of hydrogen and its subsequent impact on both the thermal and neutronic behavior of the system. The magnitudes of the neutronic-thermal impacts vary depending on input reactor power and heat pipe boundary condition; this work found reactivity impacts ranging from 100 to 3500 pcm and some minor spatial impacts to power distribution.